2026-09-26 22:03:52
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Today at a glance:
🛒 Costco: Refunds Fund Price Cuts
🍪 General Mills: Innovation Gains Traction
🫒 Darden: Olive Garden Still Lags
Costco Q4 revenue rose 11% Y/Y to $95.7 billion ($0.8 billion beat), while EPS increased 15% to $6.75. That included a $0.15 benefit from tariff refunds, so underlying EPS growth was closer to 12%. Comparable sales rose 9.4%, or 6.7% excluding gas and FX, essentially maintaining Q3’s 6.6% pace.
Fuel remained a tailwind, adding roughly 3 points to comps, but the core business was balanced. Adjusted traffic increased 3.3%, and average ticket rose 3.3%. Digitally enabled comps grew another 20%, with annual digitally enabled sales now exceeding $33 billion (~11% of Costco’s overall FY26 revenue).
Costco received $184 million in tariff refunds during Q4 and reinvested part of that into lower prices across produce, meat, beverages, furniture, and other categories. Management already received a similar amount in Q1 and plans to return most future refunds to members through better value.
Membership remains healthy, but growth continues to normalize. Paid members increased 4% to 84.1 million, while Executive memberships grew 9% to 42.3 million. US/Canada renewal improved to 92.3%. Costco also plans 33 warehouse openings in FY27, accelerating from 25 net additions in FY26.
Consumer insights from Numerator data show that Costco’s recent household growth is skewing toward Gen Z and lower-income shoppers, while lower-income customers are already making fewer trips and spending less per basket.
The stock is still expensive. Costco’s forward P/E has fallen from above 50x at its peak to roughly 41x, but that still leaves little room for error.
Bottom Line: The gasoline boost still hasn’t normalized, but Costco’s core comps are holding around 7% anyway. With membership growth returning to a more normal pace, the next leg of growth increasingly depends on getting more from each member through digital, pharmacy, Executive upgrades, and a broader services ecosystem. The market is certainly counting on it.
2026-09-25 20:00:52
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Muse launched on September 8 as Meta’s latest attempt to catch up in AI.
Despite being available only in the US and Canada, Meta’s new personal agent has reached more than 3.4 million downloads, according to Sensor Tower. Muse climbed to the top of the US app stores, and sent investors scrambling to understand what consumer agents could mean for everything from shopping and travel to banking and insurance.
At Meta Connect this week, the company made clear that Muse will not remain confined to an app, with plans to extend it across computers, communication channels, and smart devices.
Muse is Meta’s attempt to give consumers a digital Alfred. It’s “heavily inspired” by OpenClaw, according to product chief Nat Friedman. It can understand what you want, handle tasks, and increasingly act on your behalf. In doing so, Meta is making a play for the consumer AI interface that goes far beyond the chatbot.
If agents increasingly research, compare, communicate, and transact on our behalf, the implications stretch far beyond Meta. They could reshape who owns commercial intent, where advertising happens, which businesses get bypassed, and how much computing infrastructure it takes to run it all.
The internet was built around getting humans to click and scroll. The agentic internet may increasingly be built around AI choosing and acting for us.
Today at a glance:
💬 Meta’s new aggregation play
📦 Why Amazon is fighting back
🛒 Who wins when the interface moves?
🤖 Fewer clicks but more compute
What makes Muse different is that the agent can increasingly operate outside Meta’s own apps. Instead of keeping users inside Facebook, Instagram, or WhatsApp, Muse can sit between them and the rest of the internet, helping research a trip, compare products, book a restaurant, or buy something across multiple services.
These capabilities blur the boundaries between the internet’s traditional aggregators. Google aggregated information, Meta aggregated social attention, and Amazon aggregated shopping demand. Muse is trying to aggregate intent itself.
Meta wants to turn Muse into a persistent consumer service that knows your context and can keep acting on it. Meta gives the example of Muse turning a recipe saved on Instagram into a grocery list, remembering friends’ dietary restrictions, planning the dinner, and sending invitations. Those capabilities aren’t unique on their own. But combining memory, context, action, and distribution into one always-available consumer agent creates something much closer to a personal operating layer than a chatbot.
Meta is extending that idea into the physical world. Muse will come to its AI glasses, where it can act on what users see, including helping them shop for products in front of them. Meta is also dramatically expanding the hardware itself, with more than 100 glasses options expected across Ray-Ban, Oakley, and Meta Glasses by year-end.
Glasses can potentially capture the context in which intent emerges. See a product, restaurant, landmark, or problem in the physical world, and the agent increasingly has the context to understand what is happening and act from there.
Zuck’s “one more thing” made that ambition even more explicit. Muse Charm is a Tamagotchi-esque palm-sized device built specifically around Muse, giving users a way to talk to their agent without unlocking a phone or opening an app. He described it as the fastest way to access Muse when you are not wearing glasses, with Meta planning to ship it for the holidays.
The timing is notable because OpenAI is also preparing its first consumer device with former Apple design chief Jony Ive. The exact form factor remains uncertain, but Meta is already trying to establish Muse as an always-available interface before OpenAI enters the category.
Extending Muse into the physical world gives Meta access to high-intent activity close to the transaction. The company still says conversations and data inside Muse’s virtual machine are not shared with its advertising systems, reducing one obvious concern for users being asked to grant an agent access to more of their digital lives.
Meta also offered its clearest monetization roadmap yet at Connect. Zuck said Muse is free for most users, but Meta also offers $20 and $100 monthly subscription tiers for heavier usage. Meta is eventually expecting to make money by taking a small fee from transactions. That makes owning intent even more important: Meta does not necessarily need to show an ad or own the merchant if it can participate economically when Muse helps complete the purchase. Advertising could still find its way into Muse over time.
Takeaway: Meta spent two decades monetizing attention inside its own apps. Muse gives it a chance to own the layer where users express intent before money changes hands.
No company illustrates the stakes of this shift better than Amazon. It has spent decades becoming the default starting point for online shopping, owning both the final transaction and the discovery process leading up to it.
Consumers arrive knowing roughly what they want, search Amazon’s catalog, compare products, and buy. Amazon gets the data, the customer relationship, and the opportunity to monetize demand before checkout.
Muse challenges that sequence. If an agent can research products, compare options, and decide what to recommend before the user ever reaches Amazon, then Amazon may still fulfill the purchase while losing control of the journey that led there.
That helps explain why Amazon moved quickly to block Muse from shopping on Amazon.com.
The economics are substantial. Amazon generated $68.6 billion in advertising revenue last year, up 22% Y/Y. Advertising has more than doubled since 2021 (the fastest-growing segment), and now exceeds Amazon’s subscription revenue by nearly $19 billion.
For context, Amazon’s non-AWS segments generated $34.4 billion in operating income in 2025. Amazon does not disclose advertising margins, but ads alone generated twice that amount in revenue. It’s entirely plausible that Amazon’s underlying retail and logistics operations would be loss-making without the high-margin advertising layer sitting on top.
Instead of searching Amazon for headphones, imagine you tell the agent, “Find me the best noise-canceling headphones under $300 for long flights.” Muse can research the options, compare reviews and specifications, and make a recommendation before the user ever reaches Amazon.
The transaction might still happen there, but Amazon risks losing much of what happens beforehand, including search, sponsored-listing impressions, product discovery, and opportunities to increase the basket. Evidence already shows retailers give something up when an outside agent controls the experience. Walmart reportedly found that purchases through OpenAI’s Instant Checkout converted at roughly one-third the rate of customers shopping directly and produced smaller baskets.
That dynamic raises a new question about unit economics. If Muse takes a fee on purchases it completes, what does that look like? An affiliate-style take rate, a flat routing fee, or sponsored placement? Moving the interface forces a renegotiation of where the margin lives. If agents shrink baskets or reduce retailers’ ability to upsell, merchants will resist paying another toll on top.
Amazon has something Muse cannot easily replace. Over the past decade, it has built warehouses, transportation, inventory placement, and last-mile delivery infrastructure that lets it get products to customers incredibly quickly. AI can’t replace that physical network.
But that physical moat also depends on enormous volume. Amazon has spent heavily to build infrastructure with high fixed costs, which become more efficient as more purchases flow through the network. If agents gradually divert transactions toward Walmart or other retailers, every lost order spreads those costs across slightly fewer purchases.
Takeaway: Muse threatens Amazon’s control of discovery far more than its ability to fulfill the purchase. The battle is over who owns the customer before checkout.
Amazon is only one side of the story. Companies will respond very differently to agents depending on where they sit in the value chain. Shopify and PayPal are leaning in because neither depends on owning a consumer search surface filled with ads. Shopify wants its merchants and Shop Pay accessible wherever consumers choose to shop, while PayPal is opening its merchant network to Muse. Both can benefit even if Meta owns the interface.
The incentives also look different for challengers. Walmart was quick to experiment with ChatGPT shopping because, as a distant number two in e-commerce, capturing demand that might otherwise go to Amazon can be worth giving up some control over the interface. Muse may be even more attractive because Meta is not trying to own the checkout itself.
That leaves us with a useful distinction. Companies that make money by owning discovery have more to protect, while companies that make money from the transaction or infrastructure underneath it can afford to be more open to agents.
Advertising itself probably does not disappear either. It may simply move closer to the agent. We are already seeing this with ChatGPT, where Amazon advertisers can reach users inside AI conversations. Amazon extends its ad business beyond Amazon.com, while OpenAI monetizes the intent generated inside ChatGPT.
Muse could eventually create similar arrangements. Meta has promised not to feed private Muse conversations into its own advertising systems, but that does not prevent commerce partners from paying to participate in the experience with user permission. One possibility is that Meta eventually monetizes some Muse activity through someone else’s advertising or transaction infrastructure rather than immediately building its own.
The consequences extend beyond advertising. Another class of business may be exposed for a different reason. Companies that benefit from consumer inertia. A chatbot can tell someone that their savings account pays too little. An agent can identify excess cash, compare yields, and move the money. The same logic can extend to insurance, brokerage, and other categories where incumbents benefit from customers not continuously shopping for a better deal.
Agents can shorten the distance between recognizing a better option and acting on it. For banks, the real proof would eventually show up in higher deposit costs and greater movement of customer balances.
The broader shift is more important than any one partnership. Today, advertisers largely pay to influence what humans click. In an agentic world, they may increasingly pay to influence what agents consider, making product data, availability, pricing, fulfillment, and trusted merchant relationships more valuable while reducing the importance of some of the webpages and ad placements that sit between intent and purchase.
Takeaway: Agents may not eliminate advertising. They could move the most valuable ad opportunity from winning the click to earning a place in the agent’s consideration set.
There is one more economic shift. While agents simplify the internet for users, they make the underlying infrastructure much more demanding.
A traditional search might involve a few queries and clicks. An agentic task can involve repeated cycles of planning, searching, retrieving information, comparing options, calling tools, checking results, and eventually taking action. The user may see a much simpler experience, but the machine is doing considerably more work behind the scenes.
Muse’s early traction suggests consumer agents could dramatically expand the inference workload. If AI shifts from responding to discrete prompts toward agents continuously researching, communicating, and acting in the background, each user can generate considerably more computing activity.
Meta Connect offered a glimpse of what that requires. The new Muse Realtime Avatar required Meta to redesign its real-time inference stack to continuously generate synchronized voice and video. Meta says the resulting optimizations increased serving capacity 8x, allowing 12 concurrent video sessions on a single GB200 while keeping latency below one second.
The broader shift goes well beyond avatars. The first phase of the AI boom was dominated by training increasingly large models, concentrating attention on GPU accelerators. Agentic AI moves more activity toward continuous inference and orchestration, which can broaden demand into CPUs, memory, networking, and high-speed interconnects alongside GPUs.
AMD is a prime example of this architecture shift. Beyond competing in data center GPUs, its high-core-count EPYC server CPUs are increasingly important to agentic workloads, handling orchestration, data retrieval, tool calls, and the enterprise services that sit between prompts and accelerator inference. That’s one reason AMD has been a core pillar in App Economy Portfolio, where it recently became a 56-bagger and remains my largest holding.
Memory is another example. Agents continuously move between models, context, external data, and tool outputs, increasing the importance of larger pools of system memory rather than relying only on memory attached directly to accelerators.
The result is an interesting inversion:
Above the model, AI could compress the internet by reducing searches, webpages, calls, and ad surfaces.
Below the model, it could expand the infrastructure required to execute those same tasks, from compute and memory to communications, payments, and transaction software.
That's it for today.
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.
Disclosure: I own AMD, AMZN, ANET, GOOG, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-09-22 20:02:34
Welcome to the Premium edition of How They Make Money.
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In case you missed it:
OpenAI is building an advertising machine, Meta is pushing deeper into subscriptions, and Salesforce is rethinking how software gets monetized in an agentic world.
How do you make money when AI changes the interface?
AI is no longer just changing products. It’s reshaping the economics underneath them, determining who pays, what they pay for, and which parts of the stack capture the value.
This week, we look at what each company is building, why these shifts are happening now, and what they could mean for some of tech’s most durable business models.
Today at a glance:
🤖 OpenAI’s advertising machine
♾️ Meta’s second business model
☁️ Salesforce is going headless
OpenAI spent years insisting advertising was not central to its business model. It took less than 200 days to build a $1 billion advertising business.
ChatGPT Ads reached that annualized run rate in August, with tens of thousands of advertisers already using the platform. Against more than 1 billion weekly ChatGPT users, that still works out to only about $1 per user.
For a rough sense of scale, Meta generated about $57 per daily active person across its Family of Apps last year. Of course, the metrics aren’t directly comparable. Meta uses a much more engaged daily-user denominator across multiple apps, while many ChatGPT users don’t see ads at all. But the gap shows how early OpenAI’s monetization still is.
The more interesting question is how much deeper into the purchase decision ChatGPT can move.
Google became an advertising giant because search captures intent. Someone searching for accounting software is already close to a purchase. ChatGPT can potentially see much more of the decision — the budget, existing tools, requirements, preferences, and tradeoffs discussed throughout a conversation. That could make the commercial intent unusually valuable.
Amazon is already validating that thesis. The company recently opened ChatGPT Ads to brands using Amazon Ads, despite historically restricting AI chatbots from accessing much of its storefront. Amazon manages the campaigns, while OpenAI determines where the ads appear based on the ChatGPT conversation.
The arrangement is clever for both sides. Amazon preserves its advertising economics and remains the destination for the purchase. OpenAI gets paid for the intent generated inside ChatGPT.
In effect, Amazon can sell advertisers the opportunity to become the Amazon link inside a ChatGPT recommendation.
OpenAI is now pushing further with Sponsored Agents.
Instead of clicking an ad and navigating an advertiser’s website, users can start a clearly labeled conversation with an AI representative of the brand. The agent can answer questions, explain differences, and help the user decide before eventually sending them to the advertiser when they are ready to act. Sponsored Agents are currently being tested with select US advertisers.
Conversational ads change what the ad itself can do. Instead of merely earning the click, it can help qualify the customer before the click happens.
Here’s one way to think about the evolution of digital ads. We went from monetizing the query (blue links) to attention (the endless scroll) to monetizing more of the decision itself (AI agents).
The decision matters most in categories where purchases require personalized research, such as software, travel, financial services, education, home improvement, or expensive consumer products. The conversation can absorb more of the work that currently happens across search results, comparison sites, product pages, and sales calls.
OpenAI is also making it easier for advertisers to plug into the system. New integrations with Shopify and HubSpot span everything from product catalogs and campaign creation to lead capture and measurement.
The tension is obvious. ChatGPT works because users trust that its recommendations are based on usefulness, not on who paid for placement. OpenAI therefore needs to monetize commercial intent without blurring that distinction.
2026-09-18 20:00:45
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Jeff Bezos is buying into Liverpool FC.
Last month, the Amazon founder joined a consortium acquiring roughly a third of the English football club at a valuation north of $7 billion. Since then, record-setting sports deals have kept coming.
⚽ Liverpool FC: $7.0 billion — Bezos, Saverin, and Bhatia
🏈 Seattle Seahawks: $9.6 billion — Vinod Khosla and family
🏀 Los Angeles Lakers: $12.5 billion — Josh Kushner and Bob Iger
Just this week, Clearlake Capital also agreed to consolidate control of Chelsea FC in a deal valuing the club at roughly $6.8 billion.
These valuations look strange through a traditional investing lens. Liverpool generated £703 million in revenue last season and only £8 million in after-tax profit. European football clubs routinely reinvest most of what they earn into players, wages, and the pursuit of trophies.
Last summer, we explored those peculiar dynamics in Football Economics, including why even the world’s biggest clubs often struggle to turn enormous fan bases into consistent profits.
Yet while profits remain elusive, the assets themselves keep rising in value. FSG bought Liverpool for just £300 million in 2010. Sixteen years later, investors are looking at a ~15x return.
Today, we’ll explore why sports teams have become such coveted assets, and what Bezos may really be buying with Liverpool.
Today at a glance:
📈 Teams keep getting more valuable
🏟️ Why these assets are different
🏈 Why the NFL and NBA are more profitable
Recent deals are extreme, but not isolated. Across major sports, teams have become some of the world’s most valuable private assets.
The NFL dominates the leaderboard. 15 of the world’s 20 most valuable sports teams are NFL franchises, with the Dallas Cowboys leading at $15.5 billion. The only outsiders are three NBA teams and two MLB teams.
European football is notably absent.
Even Real Madrid, the world’s most valuable football club, is worth an estimated $7.7 billion according to Sportico, below every team shown above. Forbes pegs the club valuation at $9.5 billion, but either way, the contrast is striking for the world’s most popular sport.
The average NFL franchise is now worth about $9.3 billion, nearly four times the $2.4 billion average Premier League club. NBA teams command more than twice the valuation, while even MLB sits comfortably ahead.
European football generated a record €40.2 billion in revenue last season, up 6% Y/Y, including nearly a quarter from the Premier League alone. The sport has moved well beyond its post-pandemic recovery as commercial revenue, expanded competitions, and global audiences continue to grow.
Yet team values are rising much faster than the businesses underneath them.
That leaves us with the more interesting question: why does an NFL or NBA franchise command such a large premium over a Premier League club?
Only a finite number of sports teams are truly compelling.
You can start another software company, restaurant chain, or even another football club. But you cannot recreate Liverpool FC. Its history, trophies, supporters, rivalries, and place in the culture have accumulated over more than a century.
That scarcity is becoming more valuable for three reasons:
🔒 Supply is fixed. Only so many teams have genuine global relevance, while the number of billionaires, private-equity firms, and sovereign investors who can buy them keeps growing.
📺 Live attention is scarce. Streaming and social media have fragmented entertainment, but sports still bring millions of people together at the same time. That makes elite games unusually valuable to broadcasters, streamers, advertisers, and sponsors.
🌍 Monetization is global. Anfield holds about 60,000 fans, but Liverpool can monetize supporters globally through sponsorships, merchandise, tours, licensing, and digital media. The stadium caps matchday revenue, but the brand reaches far beyond.
That last point is becoming increasingly important. Among the world’s 20 highest-revenue football clubs, commercial revenue has overtaken broadcasting as the largest income source, as clubs turn global followings into sponsorship, merchandise, and other brand revenue.
Liverpool itself offered a fresh example this month, signing Turkish Airlines as its next main shirt sponsor in a five-year deal reportedly worth more than £300 million.
Sports teams are also unusual because buyers don't necessarily maximize next year’s earnings. Returns can come from cash flow, long-term asset appreciation, and the non-financial value of ownership—prestige, access, influence, or simply owning a team capable of winning trophies.
That makes a club like Liverpool an unusual asset: scarce by definition, but increasingly monetizable.
Scarcity helps explain why sports teams keep appreciating. But it does not explain why the average NFL team is worth almost four times as much as the average Premier League club. For that, we need to look at the economics.
At first, the valuation gap might seem easy to explain. You might assume American teams simply generate far more revenue. They don’t.
The average NFL franchise generates roughly $720 million a year in revenue, well ahead of the pack. But average revenue across the NBA, MLB, and Premier League is remarkably similar, at roughly $425–$450 million per team.
That makes the valuation discrepancy more surprising. The average Premier League club generates slightly more revenue than the average NBA team, yet clubs are worth less than half as much on average.
The real difference appears further down the income statement. The average NFL team generates around $127 million in operating profit, while the NBA averages roughly $113 million. Premier League clubs average only about $17 million.
Why are Premier League teams barely breaking even? The leagues create very different incentives to spend.
🪂 Failure has consequences. An NFL or NBA team can finish last and return the following season with its league membership and national revenue intact. Conversely, a Premier League club can be relegated. The all-important UEFA Champions League involves tens of millions in revenue at stake, but only for the teams finishing in the top four or five. Football teams therefore have to spend partly to protect the revenue they already have.
💰 Player spending is constrained. The NFL and NBA use salary caps and other spending rules that prevent owners from endlessly bidding against one another for talent. European football has introduced tighter financial rules, but clubs still have enormous incentives to funnel incremental revenue into wages and transfers.
📺 Revenue is shared. US leagues redistribute a large portion of national media revenue across teams, creating a much higher and more predictable floor. Every NFL team received about $453 million in shared league revenue last season, regardless of whether it won the Super Bowl or finished near the bottom.
The contrast can become almost comical. A Premier League club fighting relegation may spend heavily in January because losing could threaten the economics of the entire business. An NBA team whose season has slipped away can instead prioritize younger players and draft position, because losing games does not threaten its place in the league.
That difference in urgency matters. In European football, additional revenue often becomes additional player spending because standing still means falling behind.
Liverpool’s FY25 revenue flowed back into wages, player costs, and the infrastructure required to remain one of Europe’s elite clubs. Cutting those costs dramatically might improve margins today, but it could also reduce the odds of qualifying for the Champions League tomorrow.
The NFL and NBA have effectively designed that arms race out of their economics. Owners still compete fiercely to win, but the rules make it much harder to compete away all the profit. That helps explain why similar revenue levels can support dramatically different valuations.
That brings us back to Bezos and Liverpool.
Liverpool combines something increasingly rare: a global audience, a century of brand equity, and a place in competitions that billions of people care about. Its economics may never resemble those of an NFL franchise, but the opportunity extends far beyond next year’s profit through higher media rights, commercial growth, and continued appreciation of the club itself.
That is the common thread across Liverpool, the Lakers, and the NFL’s most valuable franchises. Their economics differ dramatically, but truly iconic teams remain in fixed supply while the number of investors who can own them keeps growing.
So Bezos is not buying Liverpool for the earnings it produces today.
He is buying an asset the world cannot make more of.
That’s it for today.
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.
Disclosure: I own AAPL, AMZN, GOOG, and META in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-09-15 20:03:50
Welcome to the Premium edition of How They Make Money.
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If you have been watching the race to power the AI boom, this is one of the most aggressive bets on physical infrastructure yet.
The stakes just got higher. With AI leaders like Dario Amodei and Sam Altman openly discussing a more cautious pace at the frontier, investors face a fundamental question. How durable is the physical infrastructure buildout if frontier development slows?
SB Energy sits right at the center of that question.
The company generated just $139 million of revenue in the first half of 2026. It now reports roughly $439 billion of contracted backlog, almost entirely tied to data centers.
There’s one catch. None of those data centers are operating yet.
SB Energy started as a renewable-power developer. The AI boom has transformed it into something much bigger. It now develops gigawatt-scale data centers and the power infrastructure behind them, with OpenAI as its largest customer and NVIDIA and SoftBank helping fund the buildout.
In effect, SB Energy is becoming a physical landlord of the AI boom, owning the infrastructure underneath the compute.
The gap between SB Energy’s revenue today and the future revenue already under contract is the entire IPO story.
I condensed 300+ pages of the S-1 into a clean breakdown, supported by our signature visuals. By the end, you'll have a clear view of the SB Energy investment case.
Today at a glance:
Overview
Business model
Financial highlights
Risks & challenges
Management
Use of proceeds
Future outlook
Personal take
SB Energy’s transformation is easy to see in one place: Milam County, Texas.
In 2024, the company completed the Orion Solar Belt, a 900 MW solar complex with Google as its anchor customer. Two years later, SB Energy is building a 1.2 GW data center campus for OpenAI in the exact same county.
That captures the entire evolution of the company. SB Energy went from supplying clean power to third-party facilities to building and owning the physical data centers themselves. Milam County was only the beginning.
SB Energy was founded in 2019 as a SoftBank Group company focused on large-scale renewable energy. It spent its early years mastering the hardest parts of infrastructure development: finding land, securing grid connections, contracting power, arranging project debt, and managing construction over multi-year cycles.
Those capabilities once looked like the plumbing of a slow-moving utility business. The AI boom changed that overnight.
Hyperscalers need massive compute, but chips are only part of the bottleneck. Gigawatt-scale data centers require land, high-voltage electricity, transmission access, environmental permits, and years of buildout. SB Energy already knew how to solve many of those problems.
Instead of remaining an upstream power supplier, the company moved up the stack. It now operates as an integrated data center and power platform, developing the physical infrastructure where AI hardware will live.
The scale changed rapidly. At its PORTS-Pike campus in Ohio, SB Energy has contracted 8 GW of capacity for OpenAI, with NVIDIA providing the compute infrastructure and initial phases coming online in 2028.
Across its portfolio, the company reports roughly 5 GW of power projects operating or under construction, along with three AI campuses in development.
SB Energy doesn't design chips, train AI models, or rent cloud instances. Its bet is on the land, power, and buildings that AI systems require.
Takeaway: SB Energy spent its first five years learning how to build power infrastructure at utility scale. The AI boom turned that expertise into physical infrastructure for the AI economy.
SB Energy monetizes that infrastructure through three segments:
2026-09-12 22:03:08
Welcome to the Saturday PRO edition of How They Make Money.
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📊 Monthly reports: 200+ companies visualized.
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Today at a glance:
🎨 Adobe: 100M Free Creators
🐶 Chewy: Treats Get Cut
📈 WealthFront: Assets Outrun Revenue
Adobe Q3 revenue rose 13% Y/Y to $6.8 billion ($60 million beat), with non-GAAP EPS of $6.13 ($0.04 beat). Total ARR grew 11% Y/Y to $27.5 billion. Margins expanded, while operating cash flow hit a Q3 record of $2.5 billion.
Creative freemium MAU surpassed 100 million, up more than 70% Y/Y, while Adobe crossed 1 billion total monthly active users. AI-first ARR also climbed above $650 million, growing more than 150% Y/Y, but still only 2% of the total.
The audience growth comes with a near-term tradeoff. Management acknowledged that the shift toward freemium contributed to slower net new ARR and RPO growth, as Adobe prioritizes acquisition and engagement before monetization. Agentic products and credit-based AI usage remain the main path to converting that engagement into revenue.
Adobe also named Anil Chakravarthy as its next CEO, effective December 1, with Shantanu Narayen moving to Executive Chair. Chakravarthy currently leads Adobe’s Customer Experience business, signaling continuity around the push toward AI-powered workflows across creative and enterprise products.
Adobe raised FY26 revenue guidance slightly to $26.58–$26.63 billion (from $26.50–$26.60 billion). Q4 revenue guidance was slightly below expectations, sending shares modestly lower.
Bottom Line: Adobe is proving it can still attract users in the AI era. The harder part is converting them. Freemium creative users have now crossed 100 million and AI-first ARR is growing rapidly, but total ARR is still expanding only around 11%. The next phase of the story is monetization rather than adoption.